ArticleNeuroImage2016
A multi-layer network approach to MEG connectivity analysis.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
82 citing papers in PubMed, 219 citations in OpenAlex.
- The Acute Effect of Methylphenidate Ingestion in a Visuomotor Task at Absolute Power Alpha in Healthy Subjects.Arquivos de neuro-psiquiatria · 2026Trial
- Changes in electrophysiological markers of cognitive control after administration of galantamine.NeuroImage. Clinical · 2018Trial
- Revolutionizing Pediatric Neurophysiology With Magnetoencephalography.Psychophysiology · 2026Review
- The role of white matter myelin in structural-functional network coupling.Communications biology · 2026Article
- Structure-function multilayer network integration and cognition in multiple sclerosis.Network neuroscience (Cambridge, Mass.) · 2026Article
- Human brain imaging with high-density electroencephalography: Techniques and applications.The Journal of physiology · 2026Review
- Self-organization and circular causation as synergetic foundations of social interaction: a multilayer network perspective.Frontiers in network physiology · 2026Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Musicians' brains at rest: multilayer network analysis of magnetoencephalography data.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Cortical parcellation optimized for magnetoencephalography with a clustering technique.Scientific reports · 2025Article
- Spatiotemporal whole-brain activity and functional connectivity of melodies recognition.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Linking structural and functional changes during aging using multilayer brain network analysis.Communications biology · 2024Article
- Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes.Human brain mapping · 2024Article
- Hyper-brain hyper-frequency network topology dynamics when playing guitar in quartet.Frontiers in human neuroscience · 2024Article
- CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.Frontiers in neuroscience · 2024Article
- The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- The longitudinal relation between executive functioning and multilayer network topology in glioma patients.Brain imaging and behavior · 2023Article
- Investigating predictive coding in younger and older children using MEG and a multi-feature auditory oddball paradigm.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Community detection in multi-frequency EEG networks.Scientific reports · 2023Article
- Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.Frontiers in neuroscience · 2023Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.